Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Sunday 21 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 150,688 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 150,678 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 7
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 3
## 111 2020-06-19 East of England 2
## 112 2020-03-01 London 0
## 113 2020-03-02 London 0
## 114 2020-03-03 London 0
## 115 2020-03-04 London 0
## 116 2020-03-05 London 0
## 117 2020-03-06 London 1
## 118 2020-03-07 London 0
## 119 2020-03-08 London 0
## 120 2020-03-09 London 1
## 121 2020-03-10 London 0
## 122 2020-03-11 London 6
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## 125 2020-03-14 London 14
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## 127 2020-03-16 London 15
## 128 2020-03-17 London 23
## 129 2020-03-18 London 27
## 130 2020-03-19 London 25
## 131 2020-03-20 London 44
## 132 2020-03-21 London 49
## 133 2020-03-22 London 54
## 134 2020-03-23 London 63
## 135 2020-03-24 London 87
## 136 2020-03-25 London 113
## 137 2020-03-26 London 129
## 138 2020-03-27 London 130
## 139 2020-03-28 London 122
## 140 2020-03-29 London 146
## 141 2020-03-30 London 149
## 142 2020-03-31 London 181
## 143 2020-04-01 London 202
## 144 2020-04-02 London 190
## 145 2020-04-03 London 196
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## 152 2020-04-10 London 170
## 153 2020-04-11 London 178
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## 156 2020-04-14 London 144
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## 159 2020-04-17 London 100
## 160 2020-04-18 London 101
## 161 2020-04-19 London 103
## 162 2020-04-20 London 95
## 163 2020-04-21 London 94
## 164 2020-04-22 London 109
## 165 2020-04-23 London 77
## 166 2020-04-24 London 71
## 167 2020-04-25 London 58
## 168 2020-04-26 London 53
## 169 2020-04-27 London 51
## 170 2020-04-28 London 43
## 171 2020-04-29 London 44
## 172 2020-04-30 London 40
## 173 2020-05-01 London 41
## 174 2020-05-02 London 41
## 175 2020-05-03 London 36
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## 195 2020-05-23 London 6
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## 213 2020-06-10 London 7
## 214 2020-06-11 London 5
## 215 2020-06-12 London 3
## 216 2020-06-13 London 3
## 217 2020-06-14 London 2
## 218 2020-06-15 London 1
## 219 2020-06-16 London 2
## 220 2020-06-17 London 1
## 221 2020-06-18 London 2
## 222 2020-06-19 London 0
## 223 2020-03-01 Midlands 0
## 224 2020-03-02 Midlands 0
## 225 2020-03-03 Midlands 1
## 226 2020-03-04 Midlands 0
## 227 2020-03-05 Midlands 0
## 228 2020-03-06 Midlands 0
## 229 2020-03-07 Midlands 0
## 230 2020-03-08 Midlands 3
## 231 2020-03-09 Midlands 1
## 232 2020-03-10 Midlands 0
## 233 2020-03-11 Midlands 2
## 234 2020-03-12 Midlands 6
## 235 2020-03-13 Midlands 5
## 236 2020-03-14 Midlands 4
## 237 2020-03-15 Midlands 5
## 238 2020-03-16 Midlands 11
## 239 2020-03-17 Midlands 8
## 240 2020-03-18 Midlands 13
## 241 2020-03-19 Midlands 8
## 242 2020-03-20 Midlands 28
## 243 2020-03-21 Midlands 13
## 244 2020-03-22 Midlands 31
## 245 2020-03-23 Midlands 33
## 246 2020-03-24 Midlands 41
## 247 2020-03-25 Midlands 48
## 248 2020-03-26 Midlands 64
## 249 2020-03-27 Midlands 72
## 250 2020-03-28 Midlands 89
## 251 2020-03-29 Midlands 92
## 252 2020-03-30 Midlands 90
## 253 2020-03-31 Midlands 123
## 254 2020-04-01 Midlands 140
## 255 2020-04-02 Midlands 142
## 256 2020-04-03 Midlands 124
## 257 2020-04-04 Midlands 151
## 258 2020-04-05 Midlands 164
## 259 2020-04-06 Midlands 140
## 260 2020-04-07 Midlands 123
## 261 2020-04-08 Midlands 186
## 262 2020-04-09 Midlands 139
## 263 2020-04-10 Midlands 127
## 264 2020-04-11 Midlands 142
## 265 2020-04-12 Midlands 139
## 266 2020-04-13 Midlands 120
## 267 2020-04-14 Midlands 116
## 268 2020-04-15 Midlands 147
## 269 2020-04-16 Midlands 102
## 270 2020-04-17 Midlands 118
## 271 2020-04-18 Midlands 115
## 272 2020-04-19 Midlands 92
## 273 2020-04-20 Midlands 107
## 274 2020-04-21 Midlands 86
## 275 2020-04-22 Midlands 78
## 276 2020-04-23 Midlands 103
## 277 2020-04-24 Midlands 79
## 278 2020-04-25 Midlands 72
## 279 2020-04-26 Midlands 81
## 280 2020-04-27 Midlands 74
## 281 2020-04-28 Midlands 68
## 282 2020-04-29 Midlands 53
## 283 2020-04-30 Midlands 56
## 284 2020-05-01 Midlands 64
## 285 2020-05-02 Midlands 51
## 286 2020-05-03 Midlands 52
## 287 2020-05-04 Midlands 61
## 288 2020-05-05 Midlands 58
## 289 2020-05-06 Midlands 59
## 290 2020-05-07 Midlands 48
## 291 2020-05-08 Midlands 34
## 292 2020-05-09 Midlands 37
## 293 2020-05-10 Midlands 42
## 294 2020-05-11 Midlands 33
## 295 2020-05-12 Midlands 45
## 296 2020-05-13 Midlands 40
## 297 2020-05-14 Midlands 37
## 298 2020-05-15 Midlands 40
## 299 2020-05-16 Midlands 34
## 300 2020-05-17 Midlands 31
## 301 2020-05-18 Midlands 34
## 302 2020-05-19 Midlands 34
## 303 2020-05-20 Midlands 36
## 304 2020-05-21 Midlands 32
## 305 2020-05-22 Midlands 27
## 306 2020-05-23 Midlands 34
## 307 2020-05-24 Midlands 19
## 308 2020-05-25 Midlands 26
## 309 2020-05-26 Midlands 33
## 310 2020-05-27 Midlands 29
## 311 2020-05-28 Midlands 27
## 312 2020-05-29 Midlands 20
## 313 2020-05-30 Midlands 20
## 314 2020-05-31 Midlands 22
## 315 2020-06-01 Midlands 20
## 316 2020-06-02 Midlands 22
## 317 2020-06-03 Midlands 24
## 318 2020-06-04 Midlands 15
## 319 2020-06-05 Midlands 21
## 320 2020-06-06 Midlands 20
## 321 2020-06-07 Midlands 16
## 322 2020-06-08 Midlands 15
## 323 2020-06-09 Midlands 17
## 324 2020-06-10 Midlands 15
## 325 2020-06-11 Midlands 13
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## 327 2020-06-13 Midlands 6
## 328 2020-06-14 Midlands 17
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## 331 2020-06-17 Midlands 10
## 332 2020-06-18 Midlands 12
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## 339 2020-03-06 North East and Yorkshire 0
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## 342 2020-03-09 North East and Yorkshire 0
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## 345 2020-03-12 North East and Yorkshire 0
## 346 2020-03-13 North East and Yorkshire 0
## 347 2020-03-14 North East and Yorkshire 0
## 348 2020-03-15 North East and Yorkshire 2
## 349 2020-03-16 North East and Yorkshire 3
## 350 2020-03-17 North East and Yorkshire 1
## 351 2020-03-18 North East and Yorkshire 2
## 352 2020-03-19 North East and Yorkshire 6
## 353 2020-03-20 North East and Yorkshire 5
## 354 2020-03-21 North East and Yorkshire 6
## 355 2020-03-22 North East and Yorkshire 7
## 356 2020-03-23 North East and Yorkshire 9
## 357 2020-03-24 North East and Yorkshire 8
## 358 2020-03-25 North East and Yorkshire 18
## 359 2020-03-26 North East and Yorkshire 21
## 360 2020-03-27 North East and Yorkshire 28
## 361 2020-03-28 North East and Yorkshire 35
## 362 2020-03-29 North East and Yorkshire 38
## 363 2020-03-30 North East and Yorkshire 64
## 364 2020-03-31 North East and Yorkshire 60
## 365 2020-04-01 North East and Yorkshire 67
## 366 2020-04-02 North East and Yorkshire 74
## 367 2020-04-03 North East and Yorkshire 100
## 368 2020-04-04 North East and Yorkshire 105
## 369 2020-04-05 North East and Yorkshire 92
## 370 2020-04-06 North East and Yorkshire 96
## 371 2020-04-07 North East and Yorkshire 102
## 372 2020-04-08 North East and Yorkshire 107
## 373 2020-04-09 North East and Yorkshire 111
## 374 2020-04-10 North East and Yorkshire 117
## 375 2020-04-11 North East and Yorkshire 98
## 376 2020-04-12 North East and Yorkshire 84
## 377 2020-04-13 North East and Yorkshire 94
## 378 2020-04-14 North East and Yorkshire 107
## 379 2020-04-15 North East and Yorkshire 96
## 380 2020-04-16 North East and Yorkshire 103
## 381 2020-04-17 North East and Yorkshire 88
## 382 2020-04-18 North East and Yorkshire 95
## 383 2020-04-19 North East and Yorkshire 88
## 384 2020-04-20 North East and Yorkshire 100
## 385 2020-04-21 North East and Yorkshire 76
## 386 2020-04-22 North East and Yorkshire 84
## 387 2020-04-23 North East and Yorkshire 63
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## 390 2020-04-26 North East and Yorkshire 65
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## 394 2020-04-30 North East and Yorkshire 57
## 395 2020-05-01 North East and Yorkshire 64
## 396 2020-05-02 North East and Yorkshire 48
## 397 2020-05-03 North East and Yorkshire 40
## 398 2020-05-04 North East and Yorkshire 49
## 399 2020-05-05 North East and Yorkshire 40
## 400 2020-05-06 North East and Yorkshire 51
## 401 2020-05-07 North East and Yorkshire 45
## 402 2020-05-08 North East and Yorkshire 42
## 403 2020-05-09 North East and Yorkshire 44
## 404 2020-05-10 North East and Yorkshire 40
## 405 2020-05-11 North East and Yorkshire 29
## 406 2020-05-12 North East and Yorkshire 27
## 407 2020-05-13 North East and Yorkshire 28
## 408 2020-05-14 North East and Yorkshire 30
## 409 2020-05-15 North East and Yorkshire 32
## 410 2020-05-16 North East and Yorkshire 35
## 411 2020-05-17 North East and Yorkshire 26
## 412 2020-05-18 North East and Yorkshire 29
## 413 2020-05-19 North East and Yorkshire 27
## 414 2020-05-20 North East and Yorkshire 21
## 415 2020-05-21 North East and Yorkshire 33
## 416 2020-05-22 North East and Yorkshire 22
## 417 2020-05-23 North East and Yorkshire 18
## 418 2020-05-24 North East and Yorkshire 25
## 419 2020-05-25 North East and Yorkshire 21
## 420 2020-05-26 North East and Yorkshire 21
## 421 2020-05-27 North East and Yorkshire 22
## 422 2020-05-28 North East and Yorkshire 20
## 423 2020-05-29 North East and Yorkshire 25
## 424 2020-05-30 North East and Yorkshire 20
## 425 2020-05-31 North East and Yorkshire 20
## 426 2020-06-01 North East and Yorkshire 16
## 427 2020-06-02 North East and Yorkshire 22
## 428 2020-06-03 North East and Yorkshire 22
## 429 2020-06-04 North East and Yorkshire 17
## 430 2020-06-05 North East and Yorkshire 17
## 431 2020-06-06 North East and Yorkshire 21
## 432 2020-06-07 North East and Yorkshire 13
## 433 2020-06-08 North East and Yorkshire 11
## 434 2020-06-09 North East and Yorkshire 11
## 435 2020-06-10 North East and Yorkshire 18
## 436 2020-06-11 North East and Yorkshire 7
## 437 2020-06-12 North East and Yorkshire 9
## 438 2020-06-13 North East and Yorkshire 10
## 439 2020-06-14 North East and Yorkshire 11
## 440 2020-06-15 North East and Yorkshire 8
## 441 2020-06-16 North East and Yorkshire 10
## 442 2020-06-17 North East and Yorkshire 6
## 443 2020-06-18 North East and Yorkshire 7
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## 461 2020-03-17 North West 4
## 462 2020-03-18 North West 6
## 463 2020-03-19 North West 7
## 464 2020-03-20 North West 10
## 465 2020-03-21 North West 11
## 466 2020-03-22 North West 13
## 467 2020-03-23 North West 15
## 468 2020-03-24 North West 21
## 469 2020-03-25 North West 21
## 470 2020-03-26 North West 29
## 471 2020-03-27 North West 35
## 472 2020-03-28 North West 28
## 473 2020-03-29 North West 46
## 474 2020-03-30 North West 67
## 475 2020-03-31 North West 52
## 476 2020-04-01 North West 86
## 477 2020-04-02 North West 96
## 478 2020-04-03 North West 95
## 479 2020-04-04 North West 98
## 480 2020-04-05 North West 102
## 481 2020-04-06 North West 100
## 482 2020-04-07 North West 135
## 483 2020-04-08 North West 127
## 484 2020-04-09 North West 119
## 485 2020-04-10 North West 117
## 486 2020-04-11 North West 138
## 487 2020-04-12 North West 125
## 488 2020-04-13 North West 129
## 489 2020-04-14 North West 131
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## 491 2020-04-16 North West 135
## 492 2020-04-17 North West 98
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## 498 2020-04-23 North West 85
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## 500 2020-04-25 North West 65
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## 503 2020-04-28 North West 57
## 504 2020-04-29 North West 62
## 505 2020-04-30 North West 59
## 506 2020-05-01 North West 45
## 507 2020-05-02 North West 56
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## 524 2020-05-19 North West 35
## 525 2020-05-20 North West 27
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## 529 2020-05-24 North West 26
## 530 2020-05-25 North West 31
## 531 2020-05-26 North West 27
## 532 2020-05-27 North West 27
## 533 2020-05-28 North West 28
## 534 2020-05-29 North West 20
## 535 2020-05-30 North West 19
## 536 2020-05-31 North West 13
## 537 2020-06-01 North West 12
## 538 2020-06-02 North West 27
## 539 2020-06-03 North West 22
## 540 2020-06-04 North West 22
## 541 2020-06-05 North West 15
## 542 2020-06-06 North West 23
## 543 2020-06-07 North West 19
## 544 2020-06-08 North West 20
## 545 2020-06-09 North West 15
## 546 2020-06-10 North West 14
## 547 2020-06-11 North West 16
## 548 2020-06-12 North West 7
## 549 2020-06-13 North West 8
## 550 2020-06-14 North West 15
## 551 2020-06-15 North West 14
## 552 2020-06-16 North West 11
## 553 2020-06-17 North West 10
## 554 2020-06-18 North West 6
## 555 2020-06-19 North West 2
## 556 2020-03-01 South East 0
## 557 2020-03-02 South East 0
## 558 2020-03-03 South East 1
## 559 2020-03-04 South East 0
## 560 2020-03-05 South East 1
## 561 2020-03-06 South East 0
## 562 2020-03-07 South East 0
## 563 2020-03-08 South East 1
## 564 2020-03-09 South East 1
## 565 2020-03-10 South East 1
## 566 2020-03-11 South East 1
## 567 2020-03-12 South East 0
## 568 2020-03-13 South East 1
## 569 2020-03-14 South East 1
## 570 2020-03-15 South East 5
## 571 2020-03-16 South East 8
## 572 2020-03-17 South East 7
## 573 2020-03-18 South East 10
## 574 2020-03-19 South East 9
## 575 2020-03-20 South East 13
## 576 2020-03-21 South East 7
## 577 2020-03-22 South East 25
## 578 2020-03-23 South East 20
## 579 2020-03-24 South East 22
## 580 2020-03-25 South East 29
## 581 2020-03-26 South East 35
## 582 2020-03-27 South East 34
## 583 2020-03-28 South East 36
## 584 2020-03-29 South East 55
## 585 2020-03-30 South East 58
## 586 2020-03-31 South East 65
## 587 2020-04-01 South East 66
## 588 2020-04-02 South East 55
## 589 2020-04-03 South East 72
## 590 2020-04-04 South East 80
## 591 2020-04-05 South East 82
## 592 2020-04-06 South East 88
## 593 2020-04-07 South East 100
## 594 2020-04-08 South East 83
## 595 2020-04-09 South East 104
## 596 2020-04-10 South East 88
## 597 2020-04-11 South East 88
## 598 2020-04-12 South East 88
## 599 2020-04-13 South East 84
## 600 2020-04-14 South East 65
## 601 2020-04-15 South East 72
## 602 2020-04-16 South East 56
## 603 2020-04-17 South East 86
## 604 2020-04-18 South East 57
## 605 2020-04-19 South East 70
## 606 2020-04-20 South East 87
## 607 2020-04-21 South East 50
## 608 2020-04-22 South East 54
## 609 2020-04-23 South East 57
## 610 2020-04-24 South East 64
## 611 2020-04-25 South East 51
## 612 2020-04-26 South East 51
## 613 2020-04-27 South East 40
## 614 2020-04-28 South East 40
## 615 2020-04-29 South East 47
## 616 2020-04-30 South East 29
## 617 2020-05-01 South East 37
## 618 2020-05-02 South East 36
## 619 2020-05-03 South East 17
## 620 2020-05-04 South East 35
## 621 2020-05-05 South East 29
## 622 2020-05-06 South East 25
## 623 2020-05-07 South East 27
## 624 2020-05-08 South East 26
## 625 2020-05-09 South East 28
## 626 2020-05-10 South East 19
## 627 2020-05-11 South East 25
## 628 2020-05-12 South East 27
## 629 2020-05-13 South East 18
## 630 2020-05-14 South East 32
## 631 2020-05-15 South East 24
## 632 2020-05-16 South East 22
## 633 2020-05-17 South East 18
## 634 2020-05-18 South East 22
## 635 2020-05-19 South East 12
## 636 2020-05-20 South East 22
## 637 2020-05-21 South East 15
## 638 2020-05-22 South East 17
## 639 2020-05-23 South East 21
## 640 2020-05-24 South East 17
## 641 2020-05-25 South East 13
## 642 2020-05-26 South East 19
## 643 2020-05-27 South East 18
## 644 2020-05-28 South East 12
## 645 2020-05-29 South East 21
## 646 2020-05-30 South East 8
## 647 2020-05-31 South East 10
## 648 2020-06-01 South East 11
## 649 2020-06-02 South East 13
## 650 2020-06-03 South East 17
## 651 2020-06-04 South East 11
## 652 2020-06-05 South East 11
## 653 2020-06-06 South East 10
## 654 2020-06-07 South East 11
## 655 2020-06-08 South East 7
## 656 2020-06-09 South East 9
## 657 2020-06-10 South East 10
## 658 2020-06-11 South East 5
## 659 2020-06-12 South East 5
## 660 2020-06-13 South East 4
## 661 2020-06-14 South East 6
## 662 2020-06-15 South East 7
## 663 2020-06-16 South East 10
## 664 2020-06-17 South East 8
## 665 2020-06-18 South East 4
## 666 2020-06-19 South East 2
## 667 2020-03-01 South West 0
## 668 2020-03-02 South West 0
## 669 2020-03-03 South West 0
## 670 2020-03-04 South West 0
## 671 2020-03-05 South West 0
## 672 2020-03-06 South West 0
## 673 2020-03-07 South West 0
## 674 2020-03-08 South West 0
## 675 2020-03-09 South West 0
## 676 2020-03-10 South West 0
## 677 2020-03-11 South West 1
## 678 2020-03-12 South West 0
## 679 2020-03-13 South West 0
## 680 2020-03-14 South West 1
## 681 2020-03-15 South West 0
## 682 2020-03-16 South West 0
## 683 2020-03-17 South West 2
## 684 2020-03-18 South West 2
## 685 2020-03-19 South West 4
## 686 2020-03-20 South West 3
## 687 2020-03-21 South West 6
## 688 2020-03-22 South West 7
## 689 2020-03-23 South West 8
## 690 2020-03-24 South West 7
## 691 2020-03-25 South West 9
## 692 2020-03-26 South West 11
## 693 2020-03-27 South West 13
## 694 2020-03-28 South West 21
## 695 2020-03-29 South West 18
## 696 2020-03-30 South West 23
## 697 2020-03-31 South West 23
## 698 2020-04-01 South West 22
## 699 2020-04-02 South West 23
## 700 2020-04-03 South West 30
## 701 2020-04-04 South West 42
## 702 2020-04-05 South West 32
## 703 2020-04-06 South West 34
## 704 2020-04-07 South West 39
## 705 2020-04-08 South West 47
## 706 2020-04-09 South West 24
## 707 2020-04-10 South West 46
## 708 2020-04-11 South West 43
## 709 2020-04-12 South West 23
## 710 2020-04-13 South West 27
## 711 2020-04-14 South West 24
## 712 2020-04-15 South West 32
## 713 2020-04-16 South West 29
## 714 2020-04-17 South West 33
## 715 2020-04-18 South West 25
## 716 2020-04-19 South West 31
## 717 2020-04-20 South West 26
## 718 2020-04-21 South West 26
## 719 2020-04-22 South West 23
## 720 2020-04-23 South West 17
## 721 2020-04-24 South West 19
## 722 2020-04-25 South West 15
## 723 2020-04-26 South West 27
## 724 2020-04-27 South West 13
## 725 2020-04-28 South West 17
## 726 2020-04-29 South West 15
## 727 2020-04-30 South West 26
## 728 2020-05-01 South West 6
## 729 2020-05-02 South West 7
## 730 2020-05-03 South West 10
## 731 2020-05-04 South West 17
## 732 2020-05-05 South West 14
## 733 2020-05-06 South West 19
## 734 2020-05-07 South West 16
## 735 2020-05-08 South West 6
## 736 2020-05-09 South West 11
## 737 2020-05-10 South West 5
## 738 2020-05-11 South West 8
## 739 2020-05-12 South West 7
## 740 2020-05-13 South West 7
## 741 2020-05-14 South West 6
## 742 2020-05-15 South West 4
## 743 2020-05-16 South West 4
## 744 2020-05-17 South West 6
## 745 2020-05-18 South West 4
## 746 2020-05-19 South West 6
## 747 2020-05-20 South West 1
## 748 2020-05-21 South West 9
## 749 2020-05-22 South West 6
## 750 2020-05-23 South West 6
## 751 2020-05-24 South West 3
## 752 2020-05-25 South West 8
## 753 2020-05-26 South West 11
## 754 2020-05-27 South West 5
## 755 2020-05-28 South West 10
## 756 2020-05-29 South West 7
## 757 2020-05-30 South West 3
## 758 2020-05-31 South West 2
## 759 2020-06-01 South West 7
## 760 2020-06-02 South West 2
## 761 2020-06-03 South West 5
## 762 2020-06-04 South West 2
## 763 2020-06-05 South West 2
## 764 2020-06-06 South West 1
## 765 2020-06-07 South West 3
## 766 2020-06-08 South West 3
## 767 2020-06-09 South West 0
## 768 2020-06-10 South West 0
## 769 2020-06-11 South West 2
## 770 2020-06-12 South West 2
## 771 2020-06-13 South West 2
## 772 2020-06-14 South West 0
## 773 2020-06-15 South West 1
## 774 2020-06-16 South West 1
## 775 2020-06-17 South West 0
## 776 2020-06-18 South West 0
## 777 2020-06-19 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 18 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.4664 -2.7431 -0.3091 2.9975 5.0309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.942e+00 5.214e-02 94.79 <2e-16 ***
## note_lag 1.166e-05 5.223e-07 22.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.99324)
##
## Null deviance: 5849.18 on 49 degrees of freedom
## Residual deviance: 541.69 on 48 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 140.023670 1.000012
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 126.280786 154.919362
## note_lag 1.000011 1.000013
Rsq(lag_mod)
## [1] 0.9073897
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.15 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0